Rede Neural Convolucional para o Diagnóstico de Leucemia

• Luis H. S. Vogado UFPI
• Rodrigo M. S. Veras UFPI
• Flavio H. D. Araujo UFPI
• Romuere R. V. Silva UFPI
• Kelson R.T. Aires UFPI

DOI: https://doi.org/10.5753/sbcas.2019.6241

Resumo

A leucemia é um tipo de câncer que afeta a produção de células san- guíneas na médula óssea o que dificulta a coagulação do sangue e o combate a infecções. Nesse trabalho propomos um método para o diagnóstico automático de leucemia utilizando Redes Neurais Convolucionais (CNNs). Nós utilizamos CNNs pré-treinadas e técnicas de transferência de aprendizagem na constru- ção do método proposto. Empregamos a técnica Deeply Fine Tuning Modi- fied (DFTM) combinada com operações de aumento de dados para refinar um modelo pré-treinado. Para treinar e testar o método proposto, utilizamos um conjunto de 2304 imagens de 14 bases diferentes. O método proposto atingiu acurácia de 98,84% e quando comparado com outros trabalhos, observamos maior robustez e consistência nos resultados. Ao final, concluímos que o ajuste fino é mais robusto a classificação de imagens heterogêneas quando comparado com a extração de características através de CNNs.

Referências

Agaian, S., Madhukar, M., and Chronopoulos, A. T. (2016). A new acute leukaemia-automated classification system. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 0(0):1–12. http://dx.doi.org/10.1080/21681163.2016.1234948

Araujo, F. H., Silva, R. R., Medeiros, F. N., Parkinson, D. D., Hexemer, A., Carneiro, C. M., and Ushizima, D. M. (2018). Reverse image search for scientific data within and beyond the visible spectrum. Expert Systems with Applications, 109:35– 48. http://dx.doi.org/10.1016/j.eswa.2018.05.015

Bejnordi, B. E., Mullooly, M., Pfeiffer, R. M., Fan, S., Vacek, P. M., Weaver, D. L., Herschorn, S., Brinton, L. A., van Ginneken, B., Karssemeijer, N., Beck, A. H., Gierach, G. L., van der Laak, J. A. W. M., and Sherman, M. E. (2018). Using deep convolutional neural networks to identify and classify tumor-associated stroma in diagnostic breast biopsies. Modern Pathology, 31:1502–1512. http://dx.doi.org/10.1038/s41379-018-0073-z

Bengio, Y., Courville, A., and Vincent, P. (2013). Representation learning: A review and new perspectives. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(8):1798–1828. http://dx.doi.org/10.1109/TPAMI.2013.50

Böhm, J. (2008). Pathologie-websites im world wide web. Der Pathologe, 29(3):231–242. http://dx.doi.org/10.1007/s00292-007-0935-5

Cortes, C. and Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3):273–297. http://dx.doi.org/10.1007/BF00994018

He, K., Zhang, X., Ren, S., and Sun, J. (2016). Deep residual learning for image recognition. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 770–778. IEEE Computer Society.

Izadyyazdanabadi, M., Belykh, E., Mooney, M., Martirosyan, N., Eschbacher, J., Nakaji, P., Preul, M., and Yang, Y. (2018). Convolutional neural networks: Ensemble modeling, fine-tuning and unsupervised semantic localization for neurosurgical cle images. Journal of Visual Communication and Image Representation, 54:10–20. http://dx.doi.org/10.1016/j.jvcir.2018.04.004

Krizhevsky, A., Sutskever, I., and Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. In Advances in Neural Information Processing Systems 25, pages 1097–1105. Curran Associates, Inc.

Labati, R. D., Piuri, V., and Scotti, F. (2011). All-idb: The acutelymphoblastic leukemia image database for image processing. In 18th IEEE International Conference on Image Processing (ICIP), pages 2045–2048. http://dx.doi.org/10.1109/ICIP.2011.6115881

Lei, D., Chen, X., and Zhao, J. (2018). Opening the black box of deep learning. CoRR, abs/1805.08355.

Madhukar, M., Agaian, S., and Chronopoulos, A. T. (2014). Automated screening system for acute myelogenous leukemia detection in blood microscopic images. IEEE Systems Journal, 8(3).

Omid Sarrafzadeh, Hossein Rabbani, A. T. H. U. B. (2014). Selection of the best features for leukocytes classification in blood smear microscopicimages. In Proc. SPIE, volume 9041, pages 9041 – 9041 – 8. http://dx.doi.org/10.1117/12.2043605

Patel, N. and Mishra, A. (2015). Automated leukaemia detection using microscopic images. Procedia Computer Science, 58:635–642. http://dx.doi.org/10.1016/j.procs.2015.08.082

Perez, L. and Wang, J. (2017). The effectiveness of data augmentation in image classification using deep learning. CoRR, abs/1712.04621.

Rehman, A., Abbas, N., Saba, T., ur Rahman, S. I., Mehmood, Z., and Kolivand, H. (2018). Classification of acute lymphoblastic leukemia using deep learning. Microscopy Research and Technique, pages 1–8. http://dx.doi.org/10.1002/jemt.23139

Rollins-Raval, M., Raval, J., and Contis, L. (2012). Experience with cellavision dm96 for peripheral blood differentials in a large multi-center academic hospital system. Journal of Pathology Informatics, 3(29):1–9. http://dx.doi.org/10.4103/2153-3539.100154

Ronneberger, O., Fischer, P., and Brox, T. (2015). U-net: Convolutional networks for biomedical image segmentation. In Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015, pages 234–241, Cham. Springer International Publishing. http://dx.doi.org/10.1007/978-3-319-24574-4_28

Sarrafzadeh, O. and Dehnavi, A. M. (2015). Nucleus and cytoplasm segmentation in microscopic images using k means clustering and region growing. Advanced Biomedical Research, pages 79–87. http://dx.doi.org/10.4103/2277-9175.163998

Sarrafzadeh, O., Rabbani, H., Dehnavi, A. M., and Talebi, A. (2015). Detecting different sub-types of acute myelogenous leukemia using dictionary learning and sparse representation. In ICIP, pages 3339–3343. IEEE. http://dx.doi.org/10.1109/ICIP.2015.7351422

Shafique, S. and Tehsin, S. (2018). Acute lymphoblastic leukemia detection and classification of its subtypes using pretrained deep convolutional neural networks. Technology in Cancer Research Treatment, 17:1–7. http://dx.doi.org/10.1177/1533033818802789

Simonyan, K. and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. CoRR, abs/1409.1556.

Singhal, V. and Singh, P. (2016). Texture Features for the Detection of Acute Lymphoblastic Leukemia, pages 535–543. Springer Singapore, Singapore.

Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z. (2016). Rethinking the inception architecture for computer vision. In 2016 IEEE Conference on Computer Vision and Pattern Recognition CVPR 2016, Las Vegas, NV, USA, June 27-30, 2016, pages 2818–2826.

Tajbakhsh, N., Shin, J. Y., Gurudu, S. R., Hurst, R. T., Kendall, C. B., Gotway, M. B., and Liang, J. (2016). Convolutional neural networks for medical image analysis: Full training or fine tuning? IEEE Transactions on Medical Imaging, 35:1299–1312. http://dx.doi.org/10.1109/TMI.2016.2535302

Thanh, T. T. P., Vununu, C., Atoev, S., Lee, S.-H., and Kwon, K.-R. (2018). Leukemia blood cell image classification using convolutional neural network. International Journal of Computer Theory and Engineering, 10(2):54–58. http://dx.doi.org/10.7763/IJCTE.2018.V10.1198

Vale, A. M. P. G., Guerreiro, A. M. G., Neto, A. D. D., Cavalvanti Junior, G. B., de Sá Leitão, V. C. L. T., and Martins, A. M. (2014). Automatic segmentation and classification of blood components in microscopic images using a fuzzy approach. Revista Brasileira de Engenharia Biomédica, 30:341 – 354. http://dx.doi.org/10.1590/1517-3151.0626

Vogado, L. H. S., Veras, R. M. S., Araújo, F. H. D., e Silva, R. R. V., and Aires, K. R. T. (2018). Leukemia diagnosis in blood slides using transfer learning in cnns and SVM for classification. Engineering Applications of Artificial Intelligence, 72:415–422. http://dx.doi.org/10.1016/j.engappai.2018.04.024

Yosinski, J., Clune, J., Bengio, Y., and Lipson, H. (2014). How transferable are features in deep neural networks? In Proceedings of the 27th International Conference on Neural Information Processing Systems - Volume 2, NIPS’14, pages 3320–3328, Cambridge, MA, USA.

Zhang, L., Lu, L., Nogues, I., Summers, R. M., Liu, S., and Yao, J. (2017). Deeppap: Deep convolutional networks for cervical cell classification. IEEE Journal of Biomedical and Health Informatics, 21(6):1633–1643. http://dx.doi.org/10.1109/JBHI.2017.2705583

Zheng, X., Wang, Y., Wang, G., and Chen, Z. (2018). Fast and robust segmentation of white blood cell images by self-supervised learning. Micron, 107:55–71. http://dx.doi.org/10.1016/j.micron.2018.01.010